Fixing Data Anomalies with Prediction Based Algorithm in Wireless Sensor Networks

Details Fixing Data Anomalies with Prediction Based Algorithm in Wireless Sensor Networks Fixing Data Anomalies with Prediction Based Algorithm in Wireless Sensor Networks

Publishing date

2011-11-14

URL

arxiv.org/abs/1111.3334v1

Science

Computer Science

Field

Distributed, Parallel, and Cluster Computing

Additional info

6 pages, 8 figures, The paper has been accepted for presentation at 7th IEEE Conference on Wireless Communication and Sensor N

Type

Scientific paper

Abstract

Data inconsistencies are present in the data collected over a large wireless sensor network (WSN), usually deployed for any kind of monitoring applications. Before passing this data to some WSN applications for decision making, it is necessary to ensure that the data received are clean and accurate. In this paper, we have used a statistical tool to examine the past data to fit in a highly sophisticated prediction model i.e., ARIMA for a given sensor node and with this, the model corrects the data using forecast value if any data anomaly exists there. Another scheme is also proposed for detecting data anomaly at sink among the aggregated data in the data are received from a particular sensor node. The effectiveness of our methods are validated by data collected over a real WSN application consisting of Crossbow IRIS Motes \cite{Crossbow:2009}.

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